ead the online documentation on decision trees and random forests in scikit$-$learn to find out how to use decision trees and random forests. Notice that training a classifier is done using the fit method, and that for decision trees this is done using a more sophisticated evolution $($known as CART$)$ of the ID$3$ algorithm covered in class.

On random seeds: Many functions in scikit$-$learn, including models as well as utilities, use randomization. For ease of grading, we will fix a random seed for questions $1,2,$ and $3$ so as to make behavior deterministic. We will use a random seed of $10.$ This can generally be done by passing in random state$=10$ to the function; please consult documentation if unsure. For cross$-$ validation methods, though, you will likely need to set the cv argument instead. You can do this by setting cv$=$KFold$($n splits$=,$random state$=10,$shuffle$=$True$)).$

$1.[10$ points$]$ Use the breast cancer data set from Homework $0$ to create a training set. Recall that the label is $0$ if the patient$$s data indicates a malignant cancer and $1$ otherwise. Compute the base rate of malignant cancer occurrence over the entire data set. In other words, what would be your best guess for the probability of malignant cancer of a single example using only the labels in the training set? This question is very simple, so try not to overthink it$.$